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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance (2nd ed.)\n",
    "\n",
    "**Mastering Data-Driven Finance**\n",
    "\n",
    "&copy; Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/py4fi_2nd_shadow.png\" width=\"300px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Why Python for Finance?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zen of Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Zen of Python, by Tim Peters\n",
      "\n",
      "Beautiful is better than ugly.\n",
      "Explicit is better than implicit.\n",
      "Simple is better than complex.\n",
      "Complex is better than complicated.\n",
      "Flat is better than nested.\n",
      "Sparse is better than dense.\n",
      "Readability counts.\n",
      "Special cases aren't special enough to break the rules.\n",
      "Although practicality beats purity.\n",
      "Errors should never pass silently.\n",
      "Unless explicitly silenced.\n",
      "In the face of ambiguity, refuse the temptation to guess.\n",
      "There should be one-- and preferably only one --obvious way to do it.\n",
      "Although that way may not be obvious at first unless you're Dutch.\n",
      "Now is better than never.\n",
      "Although never is often better than *right* now.\n",
      "If the implementation is hard to explain, it's a bad idea.\n",
      "If the implementation is easy to explain, it may be a good idea.\n",
      "Namespaces are one honking great idea -- let's do more of those!\n"
     ]
    }
   ],
   "source": [
    "import this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Python Ecosystem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * 2.5 + 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# causes intentional error\n",
    "# log(1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.log(1)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finance and Python Syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "uuid": "1447b7bb-ed26-4c0f-9e0f-222dfd5d0c9b"
   },
   "outputs": [],
   "source": [
    "S0 = 100.  \n",
    "K = 105.\n",
    "T = 1.0  \n",
    "r = 0.05  \n",
    "sigma = 0.2  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "I = 100000  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "np.random.seed(1000)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "z = np.random.standard_normal(I)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "ST = S0 * np.exp((r - sigma ** 2 / 2) * T + sigma * math.sqrt(T) * z)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "hT = np.maximum(ST - K, 0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "C0 = math.exp(-r * T) * np.mean(hT)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "uuid": "84aab05d-40de-4ef1-b3a8-eb08f86e1662"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of the European call option: 8.019.\n"
     ]
    }
   ],
   "source": [
    "print('Value of the European call option: {:5.3f}.'.format(C0))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of the European call option 7.989.\n"
     ]
    }
   ],
   "source": [
    "%run bsm_mcs_euro.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time-to-Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "uuid": "e16924db-8402-4bcb-a9c7-d5753346c72a"
   },
   "outputs": [],
   "source": [
    "import numpy as np  \n",
    "import pandas as pd  \n",
    "from pylab import plt, mpl  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn-v0_8')  \n",
    "mpl.rcParams['font.family'] = 'serif'  \n",
    "%config InlineBackend.figure_format = 'svg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "uuid": "22071d72-094b-4b51-aa39-cfa793d58623"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2138 entries, 2010-01-04 to 2018-06-29\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   .SPX    2138 non-null   float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 33.4 KB\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('../../source/tr_eikon_eod_data.csv',\n",
    "                  index_col=0, parse_dates=True)  \n",
    "data = pd.DataFrame(data['.SPX']) \n",
    "data.dropna(inplace=True)  \n",
    "data.info()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "uuid": "304b2f50-5b81-4e88-8fa7-ea69995cf2fe"
   },
   "outputs": [],
   "source": [
    "data['rets'] = np.log(data / data.shift(1))  \n",
    "data['vola'] = data['rets'].rolling(252).std() * np.sqrt(252)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "uuid": "b4e61939-c7fe-4a1e-a8c9-6fd755edcf86"
   },
   "outputs": [
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      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['.SPX', 'vola']].plot(subplots=True, figsize=(10, 6));  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Idioms & Paradigms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "uuid": "beca497d-4c20-4240-b003-4c79c26154be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "576 ms ± 5.32 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "loops = 2500000\n",
    "a = range(1, loops)\n",
    "def f(x):\n",
    "    return 3 * math.log(x) + math.cos(x) ** 2\n",
    "%timeit r = [f(x) for x in a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "uuid": "931fd1fc-cc54-4045-802f-acb8f094a23f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26 ms ± 988 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.arange(1, loops)\n",
    "%timeit r = 3 * np.log(a) + np.cos(a) ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "uuid": "8d1602b3-a490-4d1a-97d9-112825d86185"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25.5 ms ± 480 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "import numexpr as ne\n",
    "ne.set_num_threads(1)\n",
    "f = '3 * log(a) + cos(a) ** 2'\n",
    "%timeit r = ne.evaluate(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "uuid": "6994f16a-1802-4abe-851d-a48e0ead154d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.25 ms ± 170 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "ne.set_num_threads(4)\n",
    "%timeit r = ne.evaluate(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data-Driven & AI-First Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data-Driven Finance "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This sub-section requires a license for Eikon. Therefore no code is provided.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AI-First Finance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('http://hilpisch.com/tr_eikon_eod_data.csv',\n",
    "                   index_col=0, parse_dates=True)\n",
    "data = pd.DataFrame(data['AAPL.O'])  \n",
    "data['Returns'] = np.log(data / data.shift())  \n",
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "lags = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = []\n",
    "for lag in range(1, lags + 1):\n",
    "    col = 'lag_{}'.format(lag)\n",
    "    data[col] = np.sign(data['Returns'].shift(lag))  \n",
    "    cols.append(col)\n",
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(gamma='scale')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "SVC()"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "model.fit(data[cols], np.sign(data['Returns']))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Prediction'] = model.predict(data[cols])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Strategy'] = data['Prediction'] * data['Returns']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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       "<Figure size 1000x600 with 1 Axes>"
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   ],
   "source": [
    "data[['Returns', 'Strategy']].cumsum().apply(np.exp).plot(figsize=(10, 6));  "
   ]
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    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>"
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  }
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